ACO based hybrid SM-BS control of DFIG: experimental demonstration of improved dynamic performance under variable wind

Abstract Doubly fed induction generators (DFIGs) are widely used in modern wind energy systems due to their capability to efficiently extract energy over a wide range of wind speeds while enabling independent control of active and reactive power. However, ensuring high-performance operation under variable wind conditions remains challenging for conventional control strategies, which often fail to guarantee robustness, dynamic stability, and accurate power tracking simultaneously. To address these limitations, this paper proposes a hybrid control strategy combining sliding mode control (SMC) and backstepping (BS) control, with optimal tuning achieved using the ant colony optimization (ACO) algorithm. The proposed approach leverages the robustness of SMC and the smooth dynamic behavior of BS control, while reducing the chattering effect through optimized gain selection. The effectiveness of the proposed strategy is evaluated through MATLAB/Simulink simulations under realistic wind speed variations. Results demonstrate improved active and reactive power tracking, enhanced energy capture, and better power quality injected into the grid. Furthermore, the feasibility of the proposed method is validated experimentally using a dSPACE DS1104 real-time platform, confirming its practical applicability.